Mechanism of Action of microRNA-146a on Lung Cancer Cells through Notch1/Hes-1 Signaling Pathway.

Objective: We explored the mechanism of action of microRNA-146a in the lung cancer cell line A549 through the Noth1/Hes-1 signal pathway.

Methods: A549 cells were divided into a control NT group without transfection, NC group with transfection of miR-146a-NC, and MM group with transfection of miR-146a mimics. We employed qRT-PCR, MTT, Hoechst33258 fluorescence staining, Transwell assay, cell wound scratch assay, and Western blot for detection of the expression of miR-146a and Notch1/Hes-1, cell activity, apoptotic capacity, and invasion and migration abilities.

Strain energy in human tibia during different exercises with adjustable leg weights: a subject-specific computational model analysis.

Physical exercise is recommended to improve tibia strength, a common site for stress injuries, while identifying optimal training regimens remains a significant challenge. This study investigated tibial responses to varied exercise regimens using a subject-specific computational modeling approach. A subject-specific neuro-musculoskeletal model was combined with a finite element model to assess the effects of various exercises (jumping, landing, squatting, and walking) on tibial strain energy density (SED), as well as the impact of adjustable leg weights placed at different sites (shank versus thigh).

Bacterial immunotherapy leveraging IL-10R hysteresis for both phagocytosis evasion and tumor immunity revitalization.

Bacterial immunotherapy holds promising cancer-fighting potential. However, unlocking its power requires a mechanistic understanding of how bacteria both evade antimicrobial immune defenses and stimulate anti-tumor immune responses within the tumor microenvironment (TME). Here, by harnessing an engineered Salmonella enterica strain with this dual proficiency, we unveil an underlying singular mechanism.

Learning Node Representations via Sketching the Generative Process with Events Benefits Link Prediction on Heterogeneous Networks.

The Heterogeneous Information Network (HIN) stands out as a prominent tool for depicting interactions in real-world systems. Recently, representation learning on HINs has attracted significant attention, as the structured and compact output embeddings offer great convenience for network analysis and graph machine learning tasks. While existing HIN representation learning methods excel in supervised training or direct proximity reconstruction, yielding satisfactory performance in tasks like node clustering and classification, they often overlook the critical HIN generative process characterized by numerous events.

Combined BRAF G469A mutation and echinoderm microtubule associated protein like-4-anaplastic lymphoma kinase rearrangement with resistance: A case report and review of literature.

Background: Through deeper understanding of targetable driver mutations in non-small-cell lung cancer (NSCLC) over the past years, some patients with driver mutations have benefited from the targeted molecular therapies. Although the anaplastic lymphoma kinase and BRAF mutations are not frequent subtypes in NSCLC, the availability of several targeted-drugs has been confirmed through a series of clinical trials. But little is clear about the proper strategy in rare BRAF G469A mutation, not to mention co-exhibition of anaplastic lymphoma kinase and BRAF G469A mutations, which is extremely rare in NSCLC.

Kinetic models in environmental biotechnological processes: Origin, derivation and applications.

Environmental biotechnological processes encompass the utilization of microorganisms for various applications, such as wastewater treatment, bioproduct formation, and waste management. Kinetic modeling plays a crucial role in optimizing and designing these processes. This paper provides a comprehensive understanding of the kinetic models used in environmental biotechnological processes, focusing on the kinetics of microbial growth, bioproduct formation, substrate consumption, and pollutant degradation.

A study on critical dislocation of gasket for segmental linings of shield tunnels.

Dislocation of segments in shield tunnels significantly contributes to joint leakage, making it crucial to identify the critical dislocation amount of segment linings. To explore the waterproofing mechanism of sealing gaskets under water pressure, a structural coupling finite element analysis model was created. This model simulates water intrusion dynamics at segment joints, analyzing contact stress distribution and waterproof performance across various dislocation amounts.

Synaptic Density Reductions in MSA: A Potential Biomarker Identified Through [F]SynVesT-1 PET Imaging.

Objective: The objective of this study was to delineate synaptic density alterations in multiple system atrophy (MSA) and explore its potential role as a biomarker for MSA diagnosis and disease severity monitoring using [F]SynVesT-1 positron emission tomography / computed tomography (PET CT).

Methods: In this prospective study, 60 patients with MSA (30 patients with MSA-parkinsonian [MSA-P] subtype and 30 patients with MSA-cerebellar [MSA-C] subtype), 30 patients with Parkinson's disease (PD), and 30 age-matched healthy controls (HCs) underwent [F]SynVesT-1 PET/CT for synaptic density assessment. Visual, voxel, and volumetric region of interest (VOI) analyses were used to elucidate synaptic density patterns in the MSA brain and establish diagnostic criteria.

Engineering of L-threonine and L-proline biosensors by directed evolution of transcriptional regulator SerR and application for high-throughput screening.

Amino acids are important bio-based products with a multi-billion-dollar market. The development of efficient high-throughput screening technologies utilizing biosensors is essential for the rapid identification of high-performance amino acid producers. However, there remains a pressing need for biosensors that specifically target certain critical amino acids, such as L-threonine and L-proline.

Biomimetic Fingerprint-like Unclonable Optical Anticounterfeiting System with Selectively In Situ-Synthesized Perovskite Quantum Dots Embedded in Spontaneous-Phase-Separated Polymers.

Anticounterfeiting technologies meet challenges in the Internet of Things era due to the rapidly growing volume of objects, their frequent connection with humans, and the accelerated advance of counterfeiting/cracking techniques. Here, we, inspired by biological fingerprints, present a simple anticounterfeiting system based on perovskite quantum dot (PQD) fingerprint physical unclonable function (FPUF) by cooperatively utilizing the spontaneous-phase separation of polymers and selective in situ synthesis PQDs as an entropy source. The FPUFs offer red, green, and blue full-color fingerprint identifiers and random three-dimensional (3D) morphology, which extends binary to multivalued encoding by tuning the perovskite and polymer components, enabling a high encoding capacity (about 10, far surpassing that of biometric fingerprints).

Biomimetic reactive oxygen/nitrogen nanoscavengers inhibit "ferroptosis storm" and modulate immune targeting for acute kidney injury.

Cisplatin (Cis), a potent chemotherapeutic agent, often causes acute kidney injury (AKI), limiting its clinical efficacy. RONS flares at the AKI site are a key factor in its progression. In this study, leveraging the advantages of cell membrane-coated biomimetic nanocarriers, we developed a multifunctional biomimetic nanodelivery system nano-RONS-sacrificial agent for AKI treatment.

PbS Quantum Dot-Based Optoelectronic Memristors toward Multi-Task Reservoir Computing.

The rise of big data and the internet of things has driven the demand for multimodal sensing and high-efficiency low-latency processing. Inspired by the human sensory system, we present a multifunctional optoelectronic-memristor-based reservoir computing (OM-RC) system by utilizing a CuSCN/PbS quantum dots (QDs) heterojunction. The OM-RC system exhibits volatile and nonlinear responses to electrical signals and wide-spectrum optical stimuli covering ultraviolet, visible, and near-infrared (NIR) regions, enabling multitask processing of dynamic signals.

A predictive language model for SARS-CoV-2 evolution.

Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.

Corrigendum: A bibliometric analysis of interstitial cells of Cajal research.

[This corrects the article DOI: 10.3389/fmed.2024.

Evaluation of the effect of DRG payment policy based on interrupted time series modeling: evidence from a tertiary hospital in Anhui Province.

Background: The payment methodology for diagnosis-related groups (DRG) has implications for both society and medical institutions. Our study aims to analyse the impact of the reform of the payment policy according to the DRG on the operation of a tertiary hospital in Anhui Province.

Methods: Monthly data were collected from April 2020 to September 2023 during the reform period for a tertiary hospital on nine types of operational indicators, including average length of stay (ALOS), number of discharges, number of outpatient visits, percentage of discharged patients undergoing level III or IV surgery, bed turnover rate, inpatient essential drug utilization rate, low-risk group mortality, outpatient subaverage cost and inpatient subaverage cost.

Counterfactual learning for higher-order relation prediction in heterogeneous information networks.

Heterogeneous Information Networks (HINs) play a crucial role in modeling complex social systems, where predicting missing links/relations is a significant task. Existing methods primarily focus on pairwise relations, but real-world scenarios often involve multi-entity interactions. For example, in academic collaboration networks, an interaction occurs between a paper, a conference, and multiple authors.

Guidelines for selection and application of kinetics models in bioproduction processes.

Biotechnology is widely used in bioproduction to transform waste into valuable products. A comprehensive understanding of the kinetics involved is crucial for optimizing system designs. In this review, we explore various kinetics models (e.

Promote or inhibit? Transcriptomic and metabonomic insights into the effects of antibiotics on nitrogen uptake and metabolism in Myriophyllum aquaticum.

Phytoremediation is an effective nitrogen removal method to alleviate eutrophication. However, the coexistence of antibiotics may inhibit nitrogen removal by aquatic macrophytes, and the underlying mechanisms remain unclear. Here, we systematically investigated the effects of three antibiotics (norfloxacin, NOR; sulfamethoxazole, SMX; and oxytetracycline, OTC) at environmental concentrations (1 μg/L and 1 mg/L) on ammonia (NH-N) and nitrate (NO-N) removal by the aquatic macrophyte Myriophyllum aquaticum.

A proteogenomic analysis of cervical cancer reveals therapeutic and biological insights.

Although the incidence of cervical cancer (CC) has been reduced in high-income countries due to human papillomavirus (HPV) vaccination and screening strategies, it remains a significant public health issue that poses a threat to women's health in low-income countries. Here, we perform a comprehensive proteogenomic profiling of CC tumors obtained from 139 Chinese women. Integrated proteogenomic analysis links genetic aberrations to downstream pathogenesis-related pathways and reveals the landscape of HPV-associated multi-omic changes.

SEMQuant: Extending Sipros-Ensemble with Match-Between-Runs for Comprehensive Quantitative Metaproteomics.

Metaproteomics, utilizing high-throughput LC-MS, offers a profound understanding of microbial communities. Quantitative metaproteomics further enriches this understanding by measuring relative protein abundance and revealing dynamic changes under different conditions. However, the challenge of missing peptide quantification persists in metaproteomics analysis, particularly in data-dependent acquisition mode, where high-intensity precursors for MS2 scans are selected.

Comprehensive screening of industrially relevant components at genome scale using a high-quality gene overexpression collection of Corynebacterium glutamicum.

Development of efficient microbial strains for biomanufacturing requires deep understanding of the biology and functional components responsible for the synthesis, transport, and tolerance of the target compounds. A high-quality controllable gene overexpression strain collection was constructed for the industrial workhorse Corynebacterium glutamicum covering 99.7% of its genes.

zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation.

Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation.